SoroushVahidi/scidocs huggingface dataset:
Neural embedding-based indices for semantic search
(2019)

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. Email addresses: fatemeh.lashkari@unb.ca (Fatemeh Lashkari), bagheri@ryerson.ca (Ebrahim Bagheri), ghorbani@unb.ca (Ali A. Ghorbani) Preprint submitted to Information Processing and Management September 11, 2018
Visualizing RFM Segmentation
(2004)

Segmentation based on RFM (Recency, Frequency, and Monetary) has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and improve profits. RFM analysis is commonly performed using the Arthur Hughes method, which bins each of the three RFM attributes independently into five equal frequency bins. The resulting 125 cells are depicted in a tabular format or as bar graphs and analyzed by marketers, who determine the best cells (customer segments) to target. We propose an interactive visualization of RFM that helps marketers visualize and quickly identify important customer segments. Additionally, we show an integrated filtering approach that allows marketers to interactively explore the RFM segments in relation to other customer attributes, such as behavioral or demographic, to identify interesting subsegments in the customer base. We depict these RFM visualizations on two large real-world data sets and discuss how customers have used these visualizations in practice to glean interesting insights from their data. Given, the widespread use of RFM as a critical, and many times the only, segmentation tool, we believe that the proposed intuitive and interactive visualization will provide significant business value.
Prognostics and Health Management of Industrial Equipment
(2013)

Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. The present paper reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL).
Constraint-based sequential pattern mining: the pattern-growth methods
(2006)

Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our mission well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.
Bad to the bone: facial structure predicts unethical behaviour.
(2012)

Researchers spanning many scientific domains, including primatology, evolutionary biology and psychology, have sought to establish an evolutionary basis for morality. While researchers have identified social and cognitive adaptations that support ethical behaviour, a consensus has emerged that genetically determined physical traits are not reliable signals of unethical intentions or actions. Challenging this view, we show that genetically determined physical traits can serve as reliable predictors of unethical behaviour if they are also associated with positive signals in intersex and intrasex selection. Specifically, we identify a key physical attribute, the facial width-to-height ratio, which predicts unethical behaviour in men. Across two studies, we demonstrate that men with wider faces (relative to facial height) are more likely to explicitly deceive their counterparts in a negotiation, and are more willing to cheat in order to increase their financial gain. Importantly, we provide evidence that the link between facial metrics and unethical behaviour is mediated by a psychological sense of power. Our results demonstrate that static physical attributes can indeed serve as reliable cues of immoral action, and provide additional support for the view that evolutionary forces shape ethical judgement and behaviour.
Impact of social media usage on students academic performance in Saudi Arabia
(2015)

Social media is a popular method for communication amongst university students in Saudi Arabia. However excessive social media use can raise questions about whether academic performance is affected. This research explores this question by conducting a survey on university students in Saudi Arabia in regards to social media usage and their academic performance. The survey also explored which social network is the most popular amongst Saudi students, what students thought about their social media usage and factors besides social media usage which negatively affect academic performance. The survey received 108 responses and descriptive statistics including normality tests i.e. scatter plots were used to examine the relationship between the average number of hours students spent of social media a week and GPA scores of the students. The results demonstrated that there was no linear relationship between social media usage in a week and GPA score. Students highlighted that besides social media use, time management is a factor which affects students ‘studies negatively. The findings of the paper can be used to propose the effective plans for improving the academic performance of the students in such a way that a balance in the leisure, information exchange and academic performance can be maintained. 2014 Elsevier Ltd. All rights reserved.
Patterns of Play: Play-Personas in User-Centred Game Development
(2009)

In recent years certain trends from User-Centered design have been seeping into the practice of designing computer games. The balance of power between game designers and players is being renegotiated in order to find a more active role for players and provide them with control in shaping the experiences that games are meant to evoke. A growing player agency can turn both into an increased sense of player immersion and potentially improve the chances of critical acclaim. This paper presents a possible solution to the challenge of involving the user in the design of interactive entertainment by adopting and adapting the "persona" framework introduced by Alan Cooper in the field of Human Computer Interaction. The original method is improved by complementing the traditional ethnographic descriptions of personas with parametric, quantitative, data-oriented models of patterns of user behaviour for computer games. Author
Common Consensus : a web-based game for collecting commonsense goals
(2007)

In our research on Commonsense reasoning, we have found that an especially important kind of knowledge is knowledge about human goals. Especially when applying Commonsense reasoning to interface agents, we need to recognize goals from user actions (plan recognition), and generate sequences of actions that implement goals (planning). We also often need to answer more general questions about the situations in which goals occur, such as when and where a particular goal might be likely, or how long it is likely to take to achieve. In past work on Commonsense knowledge acquisition, users have been directly asked for such information. Recently, however, another approach has emerged—to entice users into playing games where supplying the knowledge is the means to scoring well in the game, thus motivating the players. This approach has been pioneered by Luis von Ahn and his colleagues, who refer to it as Human Computation. Common Consensus is a fun, self-sustaining web-based game, that both collects and validates Commonsense knowledge about everyday goals. It is based on the structure of the TV game show Family Feud1. A small user study showed that users find the game fun, knowledge quality is very good, and the rate of knowledge collection is rapid. ACM Classification: H.3.3 [INFORMATION STORAGE AND RETRIEVAL]: Information Search and Retrieval; I.2.6 [ARTIFICIAL INTELLIGENCE]: Learning
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
(2015)

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is known, and it is reasonable to assume that incorporating this information will lead to better predictions. We tackle the problem of matrix completion when pairwise relationships among variables are known, via a graph. We formulate and derive a highly efficient, conjugate gradient based alternating minimization scheme that solves optimizations with over 55 million observations up to 2 orders of magnitude faster than state-of-the-art (stochastic) gradient-descent based methods. On the theoretical front, we show that such methods generalize weighted nuclear norm formulations, and derive statistical consistency guarantees. We validate our results on both real and synthetic datasets.
Collaborative Deep Learning for Recommender Systems
(2015)

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.
Social collaborative filtering for cold-start recommendations
(2014)

We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem - recommendation for cold-start users.
Predicting purchase behaviors from social media
(2013)

In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.
Efficient Character Skew Rectification in Scene Text Images
(2014)

We present an efficient method for character skew rectification in scene text images. The method is based on a novel skew estimators, which exploit intuitive glyph properties and which can be efficiently computed in a linear time. The estimators are evaluated on a synthetically generated data (including Latin, Cyrillic, Greek, Runic scripts) and real scene text images, where the skew rectification by the proposed method improves the accuracy of a state-of-the-art scene text recognition pipeline.
Why Do Those With Long-Term Substance Use Disorders Stop Abusing Substances? A Qualitative Study
(2018)

Although a significant proportion of adults recover from substance use disorders (SUDs), little is known about how they reach this turning point or why they stop using. The purpose of the study was to explore the factors that influence reasoning and decision making about quitting substance use after a long-term SUD. Semistructured interviews were conducted with 18 participants, each of whom had been diagnosed with a SUD and had been abstinent for at least 5 years. A resource group of peer consultants in long-term recovery from SUDs contributed to the study's planning, preparation, and initial analyses. Participants recalled harmful consequences and significant events during their years of substance use. Pressure and concern from close family members were important in their initial efforts to abstain from substance use. Being able to imagine a different life, and the awareness of existing treatment options, promoted hope and further reinforced their motivation to quit. Greater focus on why those with SUDs want to quit may help direct treatment matching; treatment completion may be more likely if the person's reasons for seeking help are addressed.
Play Therapy: Practitioners' Perspectives on Implementation and Effectiveness
(2014)

The purpose of the present research was to explore practitioners’ perspectives on play therapy as an intervention when working with a child who has experienced trauma, has present PTSD symptoms and has a co-morbid mental health diagnosis. Play therapy has been accepted as an effective intervention to utilize with children who have been exposed to trauma (Schaefer, 1994). However, there is currently limited research evaluating play therapy as an intervention with children who have been traumatized and have developed PTSD or other mental health symptoms/disorders. The current study aimed to supplement the gap in existing research. Two agencies that serve early childhood mental health clients agreed to participate in the present study by completing an online survey. Data was gathered from 22 practitioner respondents. The results indicate that practitioners believe that play therapy is an effective intervention when treating children with trauma histories, PTSD symptoms, and mental health disorders. The results of the present research support findings from previous literature regarding play therapy when used as an intervention for treating trauma and/or mental health disorders. Furthermore, the present research confirms the notion that creating a safe space for their clients using play therapy is an important part of the intervention process. Given the gap in research surrounding play therapy as an intervention when PTSD and a co-morbid mental health disorders occur concurrently, further research would be beneficial to the field of social work and would positively inform the practitioners who work in early intervention settings. PRACTITIONERS’	  PERSPECTIVES 2
A Reliability Perspective of the Smart Grid
(2010)

Increasing complexity of power grids, growing demand, and requirement for greater reliability, security and efficiency as well as environmental and energy sustainability concerns continue to highlight the need for a quantum leap in harnessing communication and information technologies. This leap toward a ¿smarter¿ grid is widely referred to as ¿smart grid.¿ A framework for cohesive integration of these technologies facilitates convergence of acutely needed standards, and implementation of necessary analytical capabilities. This paper critically reviews the reliability impacts of major smart grid resources such as renewables, demand response, and storage. We observe that an ideal mix of these resources leads to a flatter net demand that eventually accentuates reliability challenges further. A gridwide IT architectural framework is presented to meet these challenges while facilitating modern cybersecurity measures. This architecture supports a multitude of geographically and temporally coordinated hierarchical monitoring and control actions over time scales from milliseconds and up.
An Approach to Generate Topic Similar Document by Seed Extraction-Based SeqGAN Training for Bait Document
(2018)

In recent years, topic similar document generation has drawn more and more attention in both academia and industry. Especially, bait document generation is very important for security. For more-like and fast bait document generation, we proposed the topic similar document generation model based on SeqGAN model (TSDG-SeqGAN). In the training phrase, we used jieba word segmentation tool for training text to greatly reduce the training time. In the generation phrase, we extract keywords and key sentence from the subject document as seeds, and then enter the seeds into the trained generation network. Next, we get keyword-based documents and documents based on key sentences from generation network. Finally, we output documents that are most similar to the subject document as the final result. Experiments show the effectiveness of our model.
NB-IoT Technology Overview and Experience from Cloud-RAN Implementation
(2017)

The 3GPP has introduced a new narrowband radio technology called narrowband Internet of Things (NB-IoT) in Release 13. NB-IoT was designed to support very low power consumption and low-cost devices in extreme coverage conditions. NB-IoT operates in very small bandwidth and will provide connectivity to a large number of low-data-rate devices. This article highlights some of the key features introduced in NB-IoT and presents performance results from real-life experiments. The experiments were carried out using an early-standard-compliant prototype based on a software defined radio partial implementation of NB-IoT that runs on a desktop computer connected to the network. It is found that a cloud radio access network is a good candidate for NB-IoT implementation.
RGB-H-CbCr skin colour model for human face detection
(2006)

While the RGB, HSV and YUV (YCbCr) are standard models used in various colour imaging applications, not all of their information are necessary to classify skin colour. This paper presents a novel skin colour model, RGB-H-CbCr for the detection of human faces. Skin regions are extracted using a set of bounding rules based on the skin colour distribution obtained from a training set. The segmented face regions are further classified using a parallel combination of simple morphological operations. Experimental results on a large photo data set have demonstrated that the proposed model is able to achieve good detection success rates for near-frontal faces of varying orientations, skin colour and background environment. The results are also comparable to that of the AdaBoost face classifier.
Multilabel SVM active learning for image classification
(2004)

Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.